n mix results

N - mixture models by species

modelling N by site to get relative abundance

abundance by site will be used as a cov on predator occupancy

7 models evaluated, dot, jdt + jdtSQ, lure + jdt + jdtSQ

Species:      rodent

Metadata Summary:

N_sites	N_counts	N_detections	rep_period	iterations	burnin	thin
127	3291	708	7 days	120000	20000	10

Detections by Year:

Yr	2016	2017	2018	2019	2020
sites	19	31	19	32	26
detections	172	202	113	108	113
N.dot.model	21	39	31	49	65

WAIC

Models by WAIC:

model	description	WAIC	N.total.est
fm7	counts	69.99641	127
fm6	lure + jdt + jdtSq	2960.26811	204
fm5	jdt + jdtSq	2994.33509	247
fm4	lure + jdt	3002.02919	202
fm2	jdt	3048.63247	251
fm1	dot	3072.02958	205
fm3	lure	3126.63791	246

Model summaries:

model: fm1

dot

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
p[1]	NA	6541	0.367	0.367	0.32	0.42	0	1.001
p[2]	NA	1909	0.214	0.225	0.15	0.27	0	1.001
p[3]	NA	3497	0.100	0.097	0.07	0.13	0	1.001
p[4]	NA	2290	0.072	0.068	0.04	0.10	0	1.001
p[5]	NA	1548	0.062	0.056	0.04	0.09	0	1.001
lambda[1]	NA	10000	1.144	1.043	0.74	1.54	0	1.001
lambda[2]	NA	2708	1.416	1.281	0.88	1.93	0	1.001
lambda[3]	NA	4988	1.782	1.632	1.10	2.45	0	1.001
lambda[4]	NA	2648	1.785	1.64	1.02	2.46	0	1.001
lambda[5]	NA	1244	2.693	2.193	1.44	3.80	0	1.001
N[36]	NA	4888	1.893	2.002	1.00	3.00	0	1.0005
N[17]	NA	10000	1.000	err	1.00	1.00	err	err
N[125]	NA	3814	3.542	2.994	1.00	6.00	0	1.0008
N[78]	NA	6396	0.733	0	0.00	2.00	1	err
N[60]	NA	6198	2.286	2.003	1.00	3.00	0	1.0008

p[1]

p[2]

p[3]

p[4]

p[5]

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[36]

N[17]

N[125]

N[78]

N[60]

model: fm2

jdt

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha	jdt	9116	0.445	0.447	0.37	0.52	0	1.001
alpha0	NA	1752	-2.232	-2.211	-2.42	-2.04	0	1.001
lambda[1]	NA	3478	3.258	3.137	2.31	4.18	0	1.001
lambda[2]	NA	3122	2.781	2.617	1.97	3.52	0	1.001
lambda[3]	NA	4982	1.826	1.77	1.20	2.40	0	1.001
lambda[4]	NA	5980	1.221	1.185	0.84	1.59	0	1.001
lambda[5]	NA	5323	1.824	1.75	1.26	2.35	0	1.001
N[114]	NA	9535	1.165	err	1.00	2.00	err	err
N[117]	NA	6212	1.779	2.002	1.00	3.00	0	1.0005
N[43]	NA	4710	4.550	4.002	3.00	6.00	0	1.001
N[102]	NA	5700	2.735	2.999	2.00	4.00	0	1.001
N[96]	NA	8366	1.128	err	1.00	2.00	err	err

alpha

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[114]

N[117]

N[43]

N[102]

N[96]

alpha relationship

model: fm3

lure

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha	lure	8110	-0.114	-0.116	-0.20	-0.03	0.13005	0.9816
alpha0	NA	1959	-2.135	-2.127	-2.32	-1.96	0	1.001
lambda[1]	NA	3726	3.473	3.373	2.47	4.48	0	1.001
lambda[2]	NA	2707	2.621	2.422	1.89	3.30	0	1.001
lambda[3]	NA	5915	1.634	1.555	1.09	2.19	0	1.001
lambda[4]	NA	5407	1.240	1.184	0.87	1.62	0	1.001
lambda[5]	NA	5753	1.709	1.667	1.19	2.21	0	1.001
N[39]	NA	7096	2.930	3	2.00	4.00	0	1.0009
N[88]	NA	9655	0.031	err	0.00	0.00	err	err
N[94]	NA	6901	1.371	1	1.00	2.00	0	1.0001
N[66]	NA	7025	1.940	err	1.00	3.00	err	err
N[68]	NA	7053	2.088	err	1.00	3.00	err	err

alpha

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[39]

N[88]

N[94]

N[66]

N[68]

alpha relationship

model: fm4

lure + jdt

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha[1]	lureDays	4712	-0.461	-0.461	-0.58	-0.35	0	1.001
alpha[2]	julianDt	5282	0.634	0.631	0.54	0.73	0	1.001
alpha0	NA	1776	-2.084	-2.083	-2.28	-1.89	0	1.001
lambda[1]	NA	3590	1.968	1.889	1.26	2.64	0	1.001
lambda[2]	NA	3694	1.956	1.872	1.37	2.57	0	1.001
lambda[3]	NA	5391	1.463	1.419	0.92	1.96	0	1.001
lambda[4]	NA	6228	1.084	1.025	0.72	1.41	0	1.001
lambda[5]	NA	5040	2.143	2.067	1.51	2.76	0	1.001
N[38]	NA	7684	1.974	1	1.00	3.00	0	1.0005
N[31]	NA	5540	1.881	err	1.00	2.00	err	err
N[4]	NA	5665	2.135	err	1.00	3.00	err	err
N[14]	NA	9118	1.034	err	1.00	1.00	err	err
N[29]	NA	8738	3.006	2.997	2.00	5.00	0	1.0009

alpha[1]

alpha[2]

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[38]

N[31]

N[4]

N[14]

N[29]

model: fm5

jdt + jdtSq

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha[1]	julianDt	2625	-0.086	-0.089	-0.22	0.05	0.62267	0.8412
alpha[2]	julianDtSq	2553	0.557	0.572	0.43	0.68	0	1.001
alpha0	NA	2071	-2.232	-2.219	-2.41	-2.05	0	1.001
lambda[1]	NA	3910	3.800	3.681	2.73	4.82	0	1.001
lambda[2]	NA	3434	2.379	2.287	1.72	3.04	0	1.001
lambda[3]	NA	5764	1.917	1.9	1.28	2.51	0	1.001
lambda[4]	NA	7056	1.129	1.094	0.78	1.47	0	1.001
lambda[5]	NA	5562	1.839	1.797	1.29	2.35	0	1.001
N[87]	NA	7820	2.588	3	2.00	4.00	0	1.0009
N[116]	NA	8382	2.729	3.002	2.00	5.00	0	1.0009
N[109]	NA	6562	1.832	2.002	1.00	3.00	0	1.0005
N[25]	NA	6636	2.597	3	2.00	4.00	0	1.0009
N[52]	NA	9611	0.062	err	0.00	0.00	err	err

alpha[1]

alpha[2]

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[87]

N[116]

N[109]

N[25]

N[52]

julian date relationship

model: fm6

lure + jdt + jdtSq

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha[1]	lureDays	5139	-0.401	-0.397	-0.50	-0.29	0.00000	1.0010
alpha[2]	julianDt	2385	0.141	0.141	-0.01	0.29	0.33039	0.9320
alpha[3]	julianDtSq	2552	0.508	0.501	0.38	0.63	0.00000	1.0010
alpha0	NA	2024	-2.131	-2.123	-2.30	-1.94	0.00000	1.0010
lambda[1]	NA	3830	2.423	2.277	1.64	3.21	0.00000	1.0010
lambda[2]	NA	4019	1.817	1.735	1.28	2.37	0.00000	1.0010
lambda[3]	NA	6151	1.608	1.491	1.05	2.14	0.00000	1.0010
lambda[4]	NA	8066	1.043	1.029	0.71	1.36	0.00000	1.0010
lambda[5]	NA	4780	2.126	2.050	1.49	2.75	0.00000	1.0010
N[123]	NA	8919	3.054	2.997	2.00	5.00	0.00000	1.0009
N[116]	NA	7914	2.960	2.001	2.00	5.00	0.00000	1.0009
N[112]	NA	8646	1.449	1.000	1.00	2.00	0.00000	1.0001
N[2]	NA	6569	2.264	2.002	1.00	3.00	0.00000	1.0008
N[27]	NA	6538	2.242	2.002	2.00	3.00	0.00000	1.0009

alpha[1]

alpha[2]

alpha[3]

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[123]

N[116]

N[112]

N[2]

N[27]

model: fm7

counts

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha	counts	7143	34.612	34.62	32.35	36.91	0	1.001
alpha0	NA	7133	2.571	2.534	2.18	2.95	0	1.001
lambda[1]	NA	10458	0.993	0.924	0.62	1.36	0	1.001
lambda[2]	NA	10000	0.998	0.965	0.70	1.31	0	1.001
lambda[3]	NA	10000	0.980	0.958	0.59	1.34	0	1.001
lambda[4]	NA	10000	0.981	0.946	0.67	1.29	0	1.001
lambda[5]	NA	10000	0.998	0.952	0.68	1.31	0	1.001
N[9]	NA	0	1.000	err	1.00	1.00	err	err
N[34]	NA	0	1.000	err	1.00	1.00	err	err
N[28]	NA	0	1.000	err	1.00	1.00	err	err
N[89]	NA	10000	1.051	err	1.00	1.00	err	err
N[82]	NA	10000	1.005	err	1.00	1.00	err	err

alpha

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[9]

## Warning in cor(X, use = "pairwise.complete.obs"): the standard deviation is zero

## Warning: Removed 50 rows containing missing values (geom_bar).

N[34]

## Warning in cor(X, use = "pairwise.complete.obs"): the standard deviation is zero

## Warning: Removed 50 rows containing missing values (geom_bar).

N[28]

## Warning in cor(X, use = "pairwise.complete.obs"): the standard deviation is zero

## Warning: Removed 50 rows containing missing values (geom_bar).

N[89]

N[82]

alpha relationship

## Warning: Removed 3 row(s) containing missing values (geom_path).